卷积神经网络
剩磁
磁光
材料科学
人工神经网络
计算机科学
人工智能
模式识别(心理学)
物理
磁场
磁化
量子力学
作者
Xiang He,Tianqi Wang,Kaixuan Wu,Haihua Liu
出处
期刊:Measurement
[Elsevier]
日期:2021-03-01
卷期号:173: 108633-108633
被引量:11
标识
DOI:10.1016/j.measurement.2020.108633
摘要
Wire arc additive metal manufacturing (WAAM) is one of the most revolutionary and popular manufacturing processes. However, the poor quality is an important factor restricting the development of this technology. In particular, it is difficult to detect the small defects on the surface and subsurface of the manufactured products. To cope with this issue, we propose a new method for automatic defects detection and classification of low carbon steel WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network. The improved remanence/magneto-optical imaging is used to obtain clear magneto-optical images. A convolutional neural network model is then deployed to detect the defects in magneto-optical images. The proposed method is effective in automatic detection of the surface defects of low-carbon steel WAAM products.
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